CI Coverage
Last updated: 2020-05-27
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Knit directory: mcfa-para-est/
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rm(list=ls())
source(paste0(getwd(),"/code/load_packages.R"))
source(paste0(getwd(),"/code/get_data.R"))
theme_set(theme_bw())sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xtable_1.8-4 kableExtra_1.1.0 cowplot_1.0.0
[4] MplusAutomation_0.7-3 data.table_1.12.8 patchwork_1.0.0
[7] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[10] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[13] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 lubridate_1.7.8 lattice_0.20-41 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 R6_2.4.1 cellranger_1.1.0
[9] plyr_1.8.6 backports_1.1.6 reprex_0.3.0 evaluate_0.14
[13] coda_0.19-3 httr_1.4.1 pillar_1.4.4 rlang_0.4.6
[17] readxl_1.3.1 rstudioapi_0.11 whisker_0.4 texreg_1.36.23
[21] gsubfn_0.7 rmarkdown_2.1 proto_1.0.0 webshot_0.5.2
[25] pander_0.6.3 munsell_0.5.0 broom_0.5.6 compiler_4.0.0
[29] httpuv_1.5.2 modelr_0.1.8 xfun_0.14 pkgconfig_2.0.3
[33] htmltools_0.4.0 tidyselect_1.1.0 viridisLite_0.3.0 fansi_0.4.1
[37] crayon_1.3.4 dbplyr_1.4.3 withr_2.2.0 later_1.0.0
[41] grid_4.0.0 nlme_3.1-147 jsonlite_1.6.1 gtable_0.3.0
[45] lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[49] scales_1.1.1 cli_2.0.2 stringi_1.4.6 fs_1.4.1
[53] promises_1.1.0 xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2
[57] vctrs_0.3.0 boot_1.3-24 tools_4.0.0 glue_1.4.1
[61] hms_0.5.3 parallel_4.0.0 yaml_2.2.1 colorspace_1.4-1
[65] rvest_0.3.5 knitr_1.28 haven_2.3.0 # take out unconverged/inadmissible cases
sim_results <- filter(sim_results, Converge==1, Admissible==1)
ids <- c("Condition", "Replication", "Estimator", "ss_l1", "ss_l2", "icc_ov", "icc_lv")
# set up vectors of variable names
pvec <- c(paste0('lambda1',1:6), paste0('lambda2',6:10), 'psiW12','psiB1', 'psiB2', 'psiB12', paste0('thetaB',1:10))
# now get standard errors
sevec <- c(paste0('selambda1',1:6), paste0('selambda2',6:10), 'sepsiW12','sepsiB1', 'sepsiB2', 'sepsiB12', paste0('sethetaB',1:10))
# stored "true" values of parameters by each condition
ptvec <- c(paste0('lambdaT1',1:6), paste0('lambdaT2',6:10), 'psiW12T', 'psiB1T', 'psiB2T', 'psiB12T', paste0("thetaBT", 1:10))
# need to reshape into "long" format and compute CIs
sim_results0 <- sim_results[,c(ids, pvec)] %>%
pivot_longer(
cols =all_of(pvec),
names_to= "parameter",
values_to = c("theta"))
sim_results1 <- sim_results[,c(ids, sevec)] %>%
pivot_longer(
cols =all_of(sevec),
names_to= "parameterSE",
values_to = c("se"))
# add new columns for lambda truth
# paste0('lambda1',1:6), paste0('lambda2',6:10)
sim_results2 <- sim_results %>%
mutate(lambdaT11=lambdaT, lambdaT26=lambdaT,
lambdaT12=lambdaT, lambdaT27=lambdaT,
lambdaT13=lambdaT, lambdaT28=lambdaT,
lambdaT14=lambdaT, lambdaT29=lambdaT,
lambdaT15=lambdaT, lambdaT210=lambdaT,
lambdaT16=lambdaT,
thetaBT1=thetaBT, thetaBT6=thetaBT,
thetaBT2=thetaBT, thetaBT7=thetaBT,
thetaBT3=thetaBT, thetaBT8=thetaBT,
thetaBT4=thetaBT, thetaBT9=thetaBT,
thetaBT5=thetaBT, thetaBT10=thetaBT)
sim_results2 <- sim_results2[,c(ids, ptvec)] %>%
pivot_longer(
cols =all_of(ptvec),
names_to= "paraTruth",
values_to = c("truth"))
sim_results0$se <- sim_results1$se
sim_results0$truth <- sim_results2$truth
zcrit <- 1.96
theta <- sim_results0$theta
se <- sim_results0$se
truth <- sim_results0$truth
ll <- theta - zcrit*se
ul <- theta + zcrit*se
contain <- data.table::fifelse(truth > ll & truth < ul, 1, 0)
sim_results0$ll <- ll
sim_results0$ul <- ul
sim_results0$contain <- contain
# recode parameter names
sim_results0$parameter <- recode(
sim_results0$parameter,
`lambda11`="lambda[1,1]", `lambda26`="lambda[2,6]",
`lambda12`="lambda[1,2]", `lambda27`="lambda[2,7]",
`lambda13`="lambda[1,3]", `lambda28`="lambda[2,8]",
`lambda14`="lambda[1,4]", `lambda29`="lambda[2,9]",
`lambda15`="lambda[1,5]", `lambda210`="lambda[2,10]",
`lambda16`="lambda[1,6]",
`thetaB1`="thetaB[1,1]", `thetaB6`="thetaB[6,6]",
`thetaB2`="thetaB[2,2]", `thetaB7`="thetaB[7,7]",
`thetaB3`="thetaB[3,3]", `thetaB8`="thetaB[8,8]",
`thetaB4`="thetaB[4,4]", `thetaB9`="thetaB[9,9]",
`thetaB5`="thetaB[5,5]", `thetaB10`="thetaB[10,10]",
`psiW12`="psiW[1,2]",`psiB12`="psiB[1,2]",
`psiB1`="psiB[1,1]", `psiB2`="psiB[2,2]"
)
level_ord <- c("lambda[1,1]", "lambda[1,2]", "lambda[1,3]", "lambda[1,4]", "lambda[1,5]", "lambda[1,6]", "lambda[2,6]", "lambda[2,7]", "lambda[2,8]", "lambda[2,9]", "lambda[2,10]", "psiW[1,2]", "psiB[1,2]", "psiB[1,1]", "psiB[2,2]", "thetaB[1,1]", "thetaB[2,2]", "thetaB[3,3]", "thetaB[4,4]", "thetaB[5,5]", "thetaB[6,6]", "thetaB[7,7]", "thetaB[8,8]", "thetaB[9,9]", "thetaB[10,10]")
sim_results0$parameter <- factor(
sim_results0$parameter,
levels=level_ord,
ordered=T)
sim_results0$parameterRev <- factor(
sim_results0$parameter,
levels=rev(level_ord),
ordered=T)
# ggplot(sim_results0, aes(y=theta,x=parameter, group=parameter))+
# geom_boxplot()+
# theme(axis.text.x = element_text(size=7, angle=60,hjust=1))
#
#
# ggplot(sim_results0, aes(y=theta,x=parameter, group=parameter))+
# geom_boxplot()+
# lims(y=c(-1,2))+
# theme(axis.text.x = element_text(size=7, angle=60,hjust=1))
# so clearly we need to remove some replications with impossible values...
sim_results0 <- sim_results0 %>%
group_by(Condition, parameter) %>%
mutate(
ni = n(),
q0.001 = quantile(theta, 0.001),
q0.01 = quantile(theta, 0.01),
q0.025 = quantile(theta, 0.025),
q0.975 = quantile(theta, 0.975),
q0.99 = quantile(theta, 0.99),
q0.999 = quantile(theta, 0.999),
flag0.975 = ifelse(theta >= q0.975 | theta <= q0.025, 1, 0),
flag0.99 = ifelse(theta >= q0.99 | theta <= q0.01, 1, 0),
flag0.999 = ifelse(theta >= q0.999 | theta <= q0.001, 1, 0)
)
sim_results1 <- filter(sim_results0, flag0.99 != 1)Plots
cols <- c("Upper Limit"="#56B4E9", "Estimate"="#CC79A7","Lower Limit"="#E69F00")
p <- ggplot(sim_results1)+
geom_boxplot(aes(y=ul,x=parameter,
group=parameter,
color="Upper Limit", fill="Upper Limit"),
outlier.shape = NA, coef = 0, alpha=0.5)+
geom_boxplot(aes(y=theta,x=parameter,
group=parameter,
color="Estimate",fill="Estimate"),
outlier.shape = NA, coef = 0, alpha=0.5)+
geom_boxplot(aes(y=ll,x=parameter,
group=parameter,
color="Lower Limit", fill="Lower Limit"),
outlier.shape = NA, coef = 0, alpha=0.5)+
geom_point(aes(y=truth,x=parameter, group= parameter),
color="red")+
facet_grid(icc_ov + icc_lv ~.) +
scale_color_manual(name=" ", values=cols)+
scale_fill_manual(name=" ", values=cols)+
lims(y=c(-0.25, 2))+
labs(y="Parameter Estimate",
title="Interquartile range plots for Estimates and CI Limits",
subtitle="Conditional on ICCs (latent and observed)")+
theme(axis.text.x = element_text(size=7, angle=60,hjust=1),
axis.title.x = element_blank(),
legend.position = "bottom")
pWarning: Removed 40712 rows containing non-finite values (stat_boxplot).Warning: Removed 2199 rows containing non-finite values (stat_boxplot).Warning: Removed 27121 rows containing non-finite values (stat_boxplot).
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xtable_1.8-4 kableExtra_1.1.0 cowplot_1.0.0
[4] MplusAutomation_0.7-3 data.table_1.12.8 patchwork_1.0.0
[7] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5
[10] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[13] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0
[16] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.1 jsonlite_1.6.1 viridisLite_0.3.0 gsubfn_0.7
[5] modelr_0.1.8 assertthat_0.2.1 pander_0.6.3 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.4 backports_1.1.6 lattice_0.20-41
[13] glue_1.4.1 digest_0.6.25 promises_1.1.0 rvest_0.3.5
[17] colorspace_1.4-1 htmltools_0.4.0 httpuv_1.5.2 plyr_1.8.6
[21] pkgconfig_2.0.3 broom_0.5.6 haven_2.3.0 scales_1.1.1
[25] webshot_0.5.2 whisker_0.4 later_1.0.0 git2r_0.27.1
[29] farver_2.0.3 generics_0.0.2 ellipsis_0.3.1 withr_2.2.0
[33] cli_2.0.2 proto_1.0.0 magrittr_1.5 crayon_1.3.4
[37] readxl_1.3.1 evaluate_0.14 fs_1.4.1 fansi_0.4.1
[41] nlme_3.1-147 xml2_1.3.2 tools_4.0.0 hms_0.5.3
[45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 compiler_4.0.0
[49] rlang_0.4.6 grid_4.0.0 rstudioapi_0.11 texreg_1.36.23
[53] labeling_0.3 rmarkdown_2.1 boot_1.3-24 gtable_0.3.0
[57] DBI_1.1.0 R6_2.4.1 lubridate_1.7.8 knitr_1.28
[61] rprojroot_1.3-2 stringi_1.4.6 parallel_4.0.0 Rcpp_1.0.4.6
[65] vctrs_0.3.0 dbplyr_1.4.3 tidyselect_1.1.0 xfun_0.14
[69] coda_0.19-3